Fine-tuning a Pre-trained Language Model (PLM) on a specific downstream task has been a well-known paradigm in natural language processing. However, with the growing size of PLMs, training the entire model on downstream tasks has become significantly time-consuming and resource-hungry. Therefore, Parameter Efficient Tuning (PET) techniques have been proposed to address the growing demand for the efficient fine-tuning of PLMs. One popular PET technique is inserting trainable adapters into a frozen model during fine-tuning. However, adapters have low-rank projections, which may reduce their representation power, resulting in sub-optimal performance. We address this problem using the Kronecker product instead of low-rank multiplications to improve the flexibility and performance of adapters. We introduce KronA, a Kronecker equivalent of LoRA for efficient fine-tuning of transformer-based PLMs. We apply the proposed adapters for fine-tuning a well-known PLM, called T5, on the GLUE benchmark to show that our method outperforms the popular PET baselines.